Cognitive network-oriented performance optimization method and system
A technology of cognitive network and optimization method, applied in transmission system, energy consumption reduction, climate sustainability, etc., can solve the problems of main user network and cognitive network interference, harmful interference of main user communication, etc.
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Embodiment 1
[0084] The cognitive network-oriented performance optimization method specifically includes the following steps: first, establish a cognitive network scene model; second, construct a spectrum sensing model of the node cluster according to the local perception of the node cluster, analyze signal changes in data transmission, and optimize Interruption performance; again, considering the influence of power allocation and node cluster head position on the interruption performance, the joint optimization of power allocation and node cluster head position is carried out. When there is an obstacle in the optimal node cluster head position, the surrounding performance is used The node cluster head assists in communication; secondly, model construction and analysis of the energy loss of the cognitive network, and introduce gamma distribution to improve the accuracy rate; finally, according to the comprehensive analysis results, select the corresponding optimization scheme.
Embodiment 2
[0086] On the basis of Embodiment 1, when each node cluster head can perform local perception, the local perception of the i-th node cluster head is:
[0087] In the formula, Indicates that the current primary user node is inactive, Indicates that the i-th cluster head receives the n-th sampling signal, Represents independent and identically distributed Gaussian noise;
[0088]
[0089] In the formula, Indicates that the primary user node is using the authorized channel, Indicates the power of the primary user node, Indicates the nth sampling signal sent by the primary user node, Indicates the complex channel gain of the sensing channel between the primary user node and the i-th secondary user node, Represents independent and identically distributed Gaussian noise.
[0090] According to spectrum sensing, nodes in a cognitive network can detect spectrum holes not occupied by primary user nodes, and then communicate data through the perceived spectrum holes. ...
Embodiment 3
[0094] On the basis of Embodiment 2, the interruption analysis in different situations is further divided into three situations. The first interruption situation occurs from the secondary user node to the node cluster head and then to the destination node, and the channel from the secondary user node to the destination node Both go through the stage of deep fading. At this time, the primary user node is inactive and the detection result of the secondary user node is correct, and the channel capacity is lower than the data transmission rate. The primary user node is inactive, and the judgment result of the secondary user node is wrong; the third interruption occurs in the stage of missing detection, at this time, the primary user node is active, and the secondary user node judges that the primary user node does not exist, And the channel capacity is lower than the data transmission rate.
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